A Harmonized Dataset for Dams and Reservoirs in West Africa

Kouassi, Valery Bessely Stanislas; Yao, Blé Anouma Fhorest; Soro, Gneneyougo Emile; Goula, Albert Bi Tié; Kelome, Nelly Carine; Klaus, Julian

doi:10.5194/essd-2026-159

Preprints

https://doi.org/10.5194/essd-2026-159

Preprints

22 Apr 2026

| 22 Apr 2026

Status: a revised version of this preprint is currently under review for the journal ESSD.

A Harmonized Dataset for Dams and Reservoirs in West Africa

Valery Bessely Stanislas Kouassi, Blé Anouma Fhorest Yao, Gneneyougo Emile Soro, Albert Bi Tié Goula, Nelly Carine Kelome, and Julian Klaus

Abstract. Most existing datasets that could support dam and reservoir management and assessments of their impacts in West Africa are limited by inaccurate georeferencing, inconsistent accessibility, heterogeneous data records, and a lack of validation against field observations. In this study, we review and assess existing datasets containing information on dams and reservoirs in West Africa and subsequently integrate them into a harmonized and consolidated regional dataset. We benchmarked the quality of the newly compiled dataset at watershed scale through an extended field study, and statistical analyses. The resulting dataset (https://doi.org/10.60507/FK2/YLDK1Y) includes 1,429 georeferenced dams and 1,258 reservoirs (with a minimum surface of 0.57 × 10^-3 km²) exceeding the count of dams and reservoirs in West Africa reported by any available dataset. It contains 38 attributes and an estimated total reservoir surface area of 14,038 km² and a cumulative storage capacity of 283,032 million cubic meter (MCM), thereby enhancing data accessibility in West Africa. The regional compiled dataset contains fewer missing entries and exhibits lower bias compared to the originate datasets, advancing the existing efforts by explicitly integrating both large- and small-scale reservoirs. The ground-based watershed scale assessment revealed strong spatial and temporal coherence for large scale reservoirs, but a systematic underrepresentation of small scale infrastructure in both the sources and thus also in the compiled dataset highlighting the importance of field validation. The field benchmarking advocates for collaborative research and data sharing initiatives among scientists and institutions across West Africa to improve the accuracy and completeness of dam and reservoir data, especially for small scale infrastructure.

Received: 26 Feb 2026 – Discussion started: 22 Apr 2026

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Valery Bessely Stanislas Kouassi, Blé Anouma Fhorest Yao, Gneneyougo Emile Soro, Albert Bi Tié Goula, Nelly Carine Kelome, and Julian Klaus

Status: final response (author comments only)

RC1:
'Comment on essd-2026-159', Anonymous Referee #1, 03 May 2026

Overall, the manuscript is well written and pleasant to read. The work is extremely interesting in a region like West Africa, where there is relatively little documentation, as it involves compiling an inventory of existing databases on the locations and characteristics of dams and reservoirs, and attempting to validate this information in the field to verify that the databases are consistent. The results could be very useful for the hydrology community, particularly for feeding into regional hydrological, taking into account water storage and irrigation processes. The database is already available online with a corresponding DOI.
One thing I'm not quite clear on is what criteria determine whether or not a database is included in the work. Do you simply use all existing data, or is there a preliminary filtering process? Similarly, I think the criteria for what is considered a large or small dam should be better defined. For example, what is the minimum reservoir size included in this new database? This is important because there is indeed a myriad of very small reservoirs, and I think it’s a real challenge to try to inventory them all. On the other hand, it’s important for database users to know what types and sizes of dams it contains. One point I think is worth elaborating on a bit more is this validation. It’s clear that the database was validated using data from a single watershed. But I think there is a need to provide the reader with a bit more information to assess the reliability of this database, particularly in different hydroclimatic contexts. I fully understand that this type of field validation is a major work that requires significant effort at the local level and, of course, cannot be carried out on a regional scale. However, we need to provide context to help verify whether this validation is representative at the regional level.
Specific comments:
Line 105: It would be helpful to explain why this basin was chosen for validation. Is it the only one with data, or is it representative of the area? In particular, we need to discuss how validation at a single site could be representative of the entire region, given that there are four very distinct climate zones.
Line 141. I don't quite understand this part. For example, the coordinates of the reservoirs were estimated using a statistical approach. That seems rather inappropriate to me. A reservoir can be observed, for example in satellite images, and its location is precise—not the result of an average estimated from a statistical distribution.
Line 286. I don't think this collection exceeds the number in previous studies; as its written it actually ranges from 1,415 to 1,429. So not a large difference. It would be interesting to state here which dataset reports 1415 dams, and the % in common with the current database. And it is not the same number reported line 488 (1141 records). Please revise.
Figure 6: I see that the size of the catchment area is missing in many cases. However, it seems to me that this information is very easy to estimate: once you have the precise locations of the dams, you can delineate the catchment area using a digital elevation model (DEM).
Line 310. It should also be noted that these are climatologically very different regions, with a gradient of aridity from south to north.
Figure 12 can be misleading. At first glance, I thought it was a comparison of the same dams—specifically, what was in the databases versus what was observed—but upon reading the text, one realizes that these are two different databases: on the one hand, what is observed in the field, and on the other hand, what is in the databases. Because otherwise, it would be very surprising that information such as the year of construction or the height of the dam—which are actually fairly easy to obtain—would differ so much. So I think the figure’s caption should be rewritten slightly to explain that these are in fact two different sets of data.
Line 415: I think this paragraph should be expanded slightly to explain the link between the spatial distribution of dams and the climate. One might imagine that dams are unnecessary in humid regions but necessary in semi-arid regions. However, this is not explicitly discussed in the manuscript. However, given the number of dams analyzed, I believe this type of analysis would be very interesting.
Line 496: I think we should clarify here what is meant by a “small reservoir.” Are we trying to detect pools a few meters in size? What is the average size? And, in fact, I feel that this definition is somewhat lacking in the manuscript; perhaps it should be clarified= what is the minimum dam size considered in the database

Citation: https://doi.org/10.5194/essd-2026-159-RC1
- AC1: 'Reply on RC1', Valery Bessely Stanislas Kouassi, 18 May 2026
  
  Good points and thank you for these constructive comments. We will address them.
  
  Citation: https://doi.org/10.5194/essd-2026-159-AC1
RC2:
'Comment on essd-2026-159', Anonymous Referee #2, 18 Jun 2026

The manuscript is generally well written and reads smoothly. It provides a clear overview of existing dam and reservoir datasets, identifies relevant scientific gaps, and presents a coherent methodology, results, and discussion. By integrating 12 existing datasets, the authors developed a harmonized database of dams and reservoirs in West Africa and evaluated it using field-survey data from the Upper Bandama watershed. The study has substantial regional relevance and potential practical value. In addition, the dataset has been made publicly available through a repository with a DOI, which is appropriate for an ESSD data description article. However, additional technical details are needed for several key procedures, including data harmonization, duplicate removal, dam–reservoir matching, and manual digitization. The authors should also consider that the source datasets differ in feature type, size threshold, and temporal coverage. Moreover, several datasets inherit records from the same sources and therefore cannot be regarded as completely independent products. These dependencies should be explicitly considered during data integration and quality assessment. Overall, I consider this a meaningful study with considerable potential value. However, the manuscript would benefit from careful revision before publication. Improving the transparency and reproducibility of the methodology would substantially enhance the scientific value and practical usability of both the manuscript and the dataset. My specific comments are provided below.

1. Abstract: Please define “small-scale infrastructure” and provide the corresponding area, storage-capacity, or dam-height threshold.

2. Introduction: The terms “large” and “small” dams and reservoirs should be precisely defined throughout the manuscript. If the classification is based on reservoir area, please provide the corresponding area ranges.

3. Line 76: The research gap is described as the “underestimation” of West African dams and reservoirs in existing datasets. Does this refer to the number of dams, reservoir area, storage capacity, positional accuracy, attribute completeness, or a combination of these factors? Please define the specific dimensions of underestimation more clearly.

4. Please explain how duplicate records were identified and removed, including the matching criteria, thresholds, and whether the process was automated or manual.

5. Please clarify whether dam points and reservoir polygons have a one-to-one relationship in the compiled dataset. How were dam points linked to reservoir polygons?

6. Please clarify whether each record retains its original data source and indicates any modifications made during harmonization.

7. Please report the sample size and selection criteria for the surveyed dams and villages in the Methods and clarify whether the survey covered all known dams in the watershed.

8. The field survey was conducted in 2024, whereas some source datasets are much older. Please consider temporal mismatches when interpreting missing dams and assessing dataset accuracy.

9. Figure 4: Please define the meaning of “lake control.”

10. Many of the selected datasets overlap or inherit information from other datasets. For example, some newer products incorporate records from GRanD, GLWD, HydroLAKES. Did the harmonization procedure account for this lack of independence?

11. Table 4: The current comparison of attribute availability is rather coarse. Please consider listing the individual attributes provided by each dataset, this should not require excessive additional space and would substantially improve the usefulness of the comparison.

12. Lines 307–311: 148,671 MCM represents approximately 52.5%, rather than 55.61%, of 283,032 MCM.

13. Lines 399–400: If the compiled dataset contains 32% of the dams observed during the field campaign, the gap should be approximately 68%, not 88%.

14. The field-based assessment was conducted in only one watershed. Please discuss more carefully whether its results can be generalized to the whole of West Africa.

15. I recommend adding a limitations subsection in discussion. This section should address the use of only one watershed for field evaluation, the non-independence of source datasets, and potential uncertainty in the field-reference data itself.

Citation: https://doi.org/10.5194/essd-2026-159-RC2
- AC2:
  'Reply on RC2', Valery Bessely Stanislas Kouassi, 07 Jul 2026
  We sincerely thank the reviewer for the careful reading of our manuscript and for the constructive and helpful comments. We are pleased that the reviewer found the manuscript generally well written and recognized the regional relevance and practical value of the harmonized dam and reservoir dataset. We also appreciate the specific suggestions regarding the need to provide more technical details on data harmonization, duplicate removal, dam–reservoir matching, manual digitization, temporal mismatches, and the non-independence of some source datasets.
  We agree that improving the transparency and reproducibility of these methodological steps will strengthen both the manuscript and the dataset. In the revised version, we will provide additional details in the Methods and Discussion sections and add a dedicated limitations subsection. Please find below our preliminary responses to each comment.
  Abstract: Please define “small-scale infrastructure” and provide the corresponding area, storage-capacity, or dam-height threshold.
  
  In our manuscript, “small-scale infrastructure” refers to minor and small reservoirs of the definitions proposed by FAO (FAO, 2001), which ranges from 0.010 to 9.99 km² surface area. To avoid any ambiguity, we will revise the manuscript to strictly follow the FAO classification, as shown below:
  Reservoir category             Surface area range at USL (km²)
  Major    ≥ 1,000
  Large     100–999
  Medium 10.0–99.9
  Small     1.00–9.99
  Minor    0.010–0.999
  Introduction: The terms “large” and “small” dams and reservoirs should be precisely defined throughout the manuscript. If the classification is based on reservoir area, please provide the corresponding area ranges.
  
  In the revised manuscript, we will clearly define these categories based on the definitions proposed by FAO (FAO, 2001).
  Line 76: The research gap is described as the “underestimation” of West African dams and reservoirs in existing datasets. Does this refer to the number of dams, reservoir area, storage capacity, positional accuracy, attribute completeness, or a combination of these factors? Please define the specific dimensions of underestimation more clearly.
  
  In the current version, the term “underestimation” refers to the number of dams and reservoirs reported in existing datasets. We will address this in the revised version of the manuscript through and improved definition or a change in terminology.
  Please explain how duplicate records were identified and removed, including the matching criteria, thresholds, and whether the process was automated or manual.
  
  The harmonization was performed manually in QGIS after merging the selected source datasets. Separate harmonized attribute tables were created for dam points and reservoir polygons. Duplicate records were identified using a combination of spatial and attribute-based checks. First, records with identical or nearly identical geographic coordinates were flagged as potential duplicates. Second, overlapping or very closely located features were visually inspected in QGIS. Third, available attributes such as dam or reservoir name, country, river or basin name, reservoir area, storage capacity, construction year, and source dataset were compared to confirm whether the records referred to the same infrastructure.
  When duplicate records were confirmed, only one representative record was retained in the compiled dataset. Attribute information from the duplicate sources was not simply discarded; rather, consistent information was retained and used to improve the completeness of the final record. In cases where attributes differed across sources, priority was given to the most complete and plausible record. Ambiguous cases were checked manually before final inclusion.
  We will revise the method section accordingly.
  Please clarify whether dam points and reservoir polygons have a one-to-one relationship in the compiled dataset. How were dam points linked to reservoir polygons?
  
  Dam points and reservoir polygons do not always have a strict one-to-one relationship in the compiled dataset. This is because the selected source datasets differ in their feature types and purposes. Some datasets provide dam locations as points only, while others provide reservoir polygons without associated dam points. In addition, some large or complex reservoir systems may include more than one dam-related point, while some small dams may not have a clearly mapped reservoir polygon in the available products.
  Where possible, dam points were linked to reservoir polygons using spatial overlay and proximity analysis in QGIS. A dam point was associated with a reservoir polygon when it was located within, on the boundary of, or immediately downstream of the polygon. This spatial matching was supported by attribute information where available, including dam or reservoir name, country, river or basin, reservoir area, and source dataset. Ambiguous cases were manually inspected using the spatial layers and, where necessary, satellite imagery or base maps.
  We will clarify this in the revised manuscript.
  Please clarify whether each record retains its original data source and indicates any modifications made during harmonization.
  
  Thank you for this important comment. We agree that data provenance is essential for the transparency and reuse of the harmonized dataset.
  In the current version of the compiled dataset, each dam or reservoir record does not retain the full list of all original datasets in which the same infrastructure may have appeared. During harmonization, when duplicate or overlapping records were identified, we retained one representative record. The source indicated for each final entry corresponds to the dataset that provided the entry itself and/or the majority of the retained attribute information. Therefore, the source field should be interpreted as the main contributing source for the harmonized record, rather than as a complete record-level provenance history across all overlapping datasets.
  However, all original datasets used to build the harmonized database are cited throughout the manuscript and are also listed in the README file associated with the published dataset. In the revised manuscript, we will clarify this point to avoid any misunderstanding. We will also explain that the main harmonization steps included standardizing attribute names, harmonizing units where needed, removing duplicate entries, and retaining the most complete and consistent information when the same dam or reservoir was reported by more than one source.
  Please report the sample size and selection criteria for the surveyed dams and villages in the Methods and clarify whether the survey covered all known dams in the watershed.
  
  We will revise the method section and provide the information that the survey covered all known dams in the area.
  The field survey was conducted in 2024, whereas some source datasets are much older. Please consider temporal mismatches when interpreting missing dams and assessing dataset accuracy.
  
  We agree that temporal mismatch is a key source of uncertainty. We will consider this in the revised manuscript when discussing missing dams and assessing the accuracy of the dataset.
  Figure 4: Please define the meaning of “lake control.
  
  The term “lake control” comes from the HydroLAKES database. It refers to a natural lake that is influenced or regulated by a control structure, such as a dam or outlet regulation structure. We will define this term in the caption of Figure 4 and/or in the relevant section of the text in the revised manuscript.
  Many of the selected datasets overlap or inherit information from other datasets. For example, some newer products incorporate records from GRanD, GLWD, HydroLAKES. Did the harmonization procedure account for this lack of independence?
  
  We agree that the selected datasets cannot all be considered fully independent. Several dam and reservoir products share common source information or inherit records from earlier datasets such as GRanD, GLWD, and HydroLAKES.
  Our harmonization procedure accounted for this issue mainly through duplicate identification and data-source tracking. When the same dam or reservoir appeared in multiple datasets, it was treated as a duplicate record rather than as independent evidence of multiple infrastructures (dams and reservoirs). The final compiled dataset therefore aimed to retain one harmonized record per identified infrastructure while preserving source information for traceability.
  We will discuss this more explicitly in the revised version.
  Table 4: The current comparison of attribute availability is rather coarse. Please consider listing the individual attributes provided by each dataset; this should not require excessive additional space and would substantially improve the usefulness of the comparison.
  
  Thank you for this useful suggestion. The existing datasets contain many individual attributes. For example, GRanD alone includes more than 45 attributes. Listing all attributes from each dataset in Table 4 would therefore make the table too large and difficult to read in the main manuscript. For this reason, we grouped the available attributes into four main categories and presented these categories in Table 4 to provide a concise overview of attribute availability across datasets.
  Lines 307–311: 148,671 MCM represents approximately 52.5%, rather than 55.61%, of 283,032 MCM.
  
  Thank you. We will correct the percentage in the revised manuscript and check all numbers.
  Lines 399–400: If the compiled dataset contains 32% of the dams observed during the field campaign, the gap should be approximately 68%, not 88%.
  
  We will correct the statement in the revised manuscript.
  The field-based assessment was conducted in only one watershed. Please discuss more carefully whether its results can be generalized to the whole of West Africa.
  
  The Upper Bandama watershed provides a valuable benchmark because it contains many small reservoirs and represents a data-scarce context where existing datasets often have limited coverage. We fully agree that this is only one watershed and does not capture the full diversity of environmental, hydrological, institutional, and data-availability conditions across West Africa, but it is currently the only validation data available to us for this assessment and thus of high value. We will address this in the revised discussion.
  I recommend adding a limitations subsection in discussion. This section should address the use of only one watershed for field evaluation, the non-independence of source datasets, and potential uncertainty in the field-reference data itself.
  
  We will add a dedicated limitations subsection to the discussion.
  
  Citation: https://doi.org/10.5194/essd-2026-159-AC2

Valery Bessely Stanislas Kouassi, Blé Anouma Fhorest Yao, Gneneyougo Emile Soro, Albert Bi Tié Goula, Nelly Carine Kelome, and Julian Klaus

Data sets

West Africa Dams and Reservoirs Dataset Valery Bessely Stanislas Kouassi et al. https://doi.org/10.60507/FK2/YLDK1Y

Valery Bessely Stanislas Kouassi, Blé Anouma Fhorest Yao, Gneneyougo Emile Soro, Albert Bi Tié Goula, Nelly Carine Kelome, and Julian Klaus

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Short summary

Dams and reservoirs are crucial for water supply, energy, and farming in West Africa, but information about them is often inaccessible, incomplete, or inconsistent. We combined existing records with field observations to create the a harmonized dataset to date, with quality verified at the watershed scale. This work improves access to dam and reservoir information in West Africa, supporting research and investment planning for climate adaptation across the region.


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